OathReady: Canadian Citizenship Test

Check or look up an answer

check_answer

Read-onlyIdempotent

Look up a question in OathReady's verified bank and optionally grade an answer to it. Accepts a question id (exact) OR remembered question text (fuzzy matched against all questions and their alternate phrasings). Typical uses: grading long after a question was fetched, or a user half-remembering a question from their real test and wanting the verified answer.

Input Schema

TableJSON Schema

Name	Required	Description
`answer`	No	The user's answer to grade, if they have one
`language`	No	Language the answer (and question_text, if given) is written in; grading compares against that language's text
`question_id`	No	The question id, e.g. q-042 (exact match)
`question_text`	No	The question as the user remembers it (fuzzy matched); used when no id is given

Output Schema

TableJSON Schema

Name	Required	Description
`question`	Yes
`web_page`	Yes
`explanation`	Yes
`question_id`	Yes
`correct_answer`	Yes
`match_confidence`	Yes
`your_answer_correct`	Yes	null when no answer was provided to grade
`other_possible_matches`	Yes	Runner-up matches when the text match was not certain

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedAug 18, 2026

addedInput schema / properties / language

Added value: +{
+  "description": "Language the answer (and question_text, if given) is written in; grading compares against that language's text",
+  "enum": [
+    "en",
+    "fr"
+  ],
+  "type": "string"
+}

First observedJul 21, 2026

TDQS

A4.4/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and idempotentHint=true, so the agent knows it's a safe read operation. The description adds valuable behavioral context: it explains the exact vs. fuzzy matching modes and that grading compares against the specified language's text. It does not contradict any annotation. It doesn't mention edge cases like what happens if both question_id and question_text are supplied, but that is a minor gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no redundant wording. It front-loads the core action and then gives typical use cases. Every clause adds information; there is no fluff or repetition of the title.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (4 optional params, fuzzy matching, grading), the description covers the essential behaviors and use cases. The output schema exists, so return details are handled elsewhere. It might benefit from clarifying behavior when both question_id and question_text are provided, but the current text is adequate for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so each parameter is already documented. The description adds important relational semantics: it explains that question_id is exact while question_text is fuzzy matched, and that language determines which language's text is used for grading. This goes beyond the individual parameter descriptions and clarifies how the parameters interact.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: look up a question in OathReady's verified bank and optionally grade an answer. It specifies the verb (look up, grade), the resource (verified bank), and the two modes (exact id or fuzzy text). It is clearly distinct from the sibling get_* tools, which retrieve content rather than grade or look up answers.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit typical use cases: 'grading long after a question was fetched' and 'a user half-remembering a question... wanting the verified answer.' This tells the agent when to use it. However, it does not explicitly contrast it with sibling tools (e.g., 'use this instead of get_practice_question when grading'), so the when-not-to-use is implicit rather than stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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